Active inference and learning

Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, John O'Doherty, Giovanni Pezzulo

Research output: Contribution to journalArticlepeer-review

225 Citations (Scopus)
25 Downloads (Pure)

Abstract

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.

Original languageEnglish
Pages (from-to)862-879
Number of pages18
JournalNeuroscience and Biobehavioral Reviews
Volume68
Early online date29 Jun 2016
DOIs
Publication statusPublished - Sep 2016

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